The embedding API built for production.
Production-grade embeddings on dedicated NVIDIA DGX infrastructure. Drop-in replacement for OpenAI.
- 87ms P50 latency · dedicated GPU, no shared queue
- OpenAI-compatible: two lines of code to switch
- Zero data retention · zero trust mTLS
- Three quality tiers: Turbo, Pro, Ultra 4K
New accounts start with 10M free tokens. No credit card.
or explore Forge features →
Program Membership
Member of NVIDIA Inception
# before client = OpenAI( base_url="https://api.openai.com/v1", api_key=os.environ["OPENAI_API_KEY"] ) # after client = OpenAI( base_url="https://api.voxell.ai/v1", api_key=os.environ["VOXELL_API_KEY"] )
Production AI is only as good as what it retrieves.
In a preregistered Stanford audit, AI legal-research tools sold as “hallucination-free” still returned wrong answers 17–33% of the time — and the breakdown traces back to retrieval. Surface the wrong passage and no model downstream can save the answer.
Our answer: embeddings ranked #1 on MTEB, so the right context surfaces first — and a stack that puts them to work end to end. Read the breakdown →
The embeddings that top MTEB, behind one API.
Problem Your embedding model underperforms on your domain — you see it in recall metrics and in user complaints.
A drop-in, OpenAI-compatible embedding API on dedicated GPUs. Two lines of code to switch, three quality tiers.
Document in, cited answer out — in one call.
Problem Grounded RAG takes a team months to build, and the answers still aren’t ones you can trust.
Upload a document, ask a question, get an answer grounded in your own text with citations back to the source. The whole retrieval pipeline, managed, on our #1 embeddings.
Real-time sync you actually own.
Problem Real-time state gets metered per message, locked to one vendor, and buried under message-queue plumbing you don’t need.
Local-first cross-device sync that runs in your own cloud — flat cost, a ~2.5 KB client, no Kafka to operate.
Under the hood: Coherence (GPU-native exact k-NN — recall 1.0, no index, the alternative to approximate ANN) and ARC (GPU-native cache, 217M ops/s) are Voxell’s resident-compute research tier. See all products →
Training your own model? Voxell Ore, the dataset behind our #1 MTEB ranking →
Ready to replace your embedding provider?
OpenAI-compatible. 10M free tokens. No migration risk.
The technical foundations behind Voxell's products.
Why I ripped out Hugging Face TEI and built qwen-embed-native, a Go + custom-CUDA embedding engine …
Voxell's MTEB(eng, v2) submission: architecture, training methodology, contamination defense, and …
Commercial benchmarking, volume pricing, or custom SLAs. Talk to us directly.